Predictive drowsiness alarm method

ABSTRACT

A method for predicting drowsiness is disclosed. By obtaining average heart beat rate values of a driver, and according to the characteristics of the heart beat rate values over a period of time, the method is utilized to determine whether the human being is going to sleep. The method comprises the following steps: detecting a heart beat rate of a driver; calculating a curve of the heart beat rate average during a time interval of X minutes; determining an accumulated period of time during which the slope values of the linear regression equations are smaller than the predetermined slope value Z; determining whether the length of the accumulated period of time is greater than a time threshold T to generate a drowsiness detecting result; and determining whether to raise an alarm based on the drowsiness detecting result.

1. TECHNICAL FIELD

The disclosure relates to a predictive drowsiness alarm method.

2. BACKGROUND

While driving long distances, drivers must remain focused for lengthyperiods of time and can easily become very tired or even fall asleep.During early stages of drowsiness, the driver may fall asleep for briefmoments. Attention lapses and reduced alertness occur for short periods(less than 30 seconds) but the driver usually awakens with an awarenessof danger. However, the driver subsequently feels weary, and continuesto drift in and out of consciousness until finally falling completelyasleep. In addition, persons working under highly dangerous conditionsin quiet environments, e.g., analysts dealing with dangerous materialsrequiring focused attention, are likely to become lethargic in a shorttime. People who become drowsy while working under such conditions mayeasily lose awareness of the dangers in their surroundings.

U.S. Pat. No. 7,088,250 discloses a fatigue-level estimation apparatusto determine a fatigue level of a driver. U.S. Pat. No. 6,070,098utilizes an observation of activities related to fatigue and determinesa level of fatigue based on a large amount of processed data. However,the detected data of a drowsy driver, such as observations of a driver'sbehavior or the reflectivity of the eyelid, may be similar to those ofan alert subject. Therefore, there is a need to reduce required dataprocessing amount and to detect drowsiness effectively, so as to meetindustrial requirements.

SUMMARY

The present disclosure provides a predictive drowsiness alarm method,which detects drowsiness of a driver in accordance with heart beat ratesover multiple time intervals.

The present disclosure provides a predictive drowsiness alarm methodcomprising the following steps: detecting a heart beat rate of a driver;calculating a curve of the heart beat rate average over a time intervalof X minutes; calculating a plurality of slopes of the linear regressionequations over a time interval of Y minutes in accordance with the curveof the heart beat rate average; determining an accumulated period oftime during which the slope values of the linear regression equationsare smaller than the predetermined slope value Z; and determiningwhether the length of the accumulated period of time is greater than atime threshold T to generate a drowsiness detecting result, wherein Xranges from 1 to 10, Y ranges from 1 to 10, Z ranges from −0.001 to−0.1, and T ranges from 60 to 600 seconds.

The present disclosure provides another predictive drowsiness alarmmethod, comprising the following steps: detecting a heart beat rate of adriver; calculating a curve of the heart beat rate average over a timeinterval of X minutes; calculating a plurality of slopes of the linearregression equations over a time interval of Y minutes in accordancewith the curve of the heart beat rate average; calculating a rate ofchange of the slopes of the linear regression equations in accordancewith a time interval, wherein the rate of change is defined as thedifference between the slope of the linear regression equations during afirst time interval and the slope of the linear regression equationsduring a second time interval; and determining whether the rate ofchange is smaller than a predetermined threshold M of the rate of changeto generate a drowsiness detecting result, wherein M ranges from −0.001to −0.1, X ranges from 1 to 10, and Y ranges from 1 to 10.

The present disclosure provides another predictive drowsiness alarmmethod, comprising the following steps: detecting a heart beat rate of adriver; calculating a curve of the heart beat rate average over a timeinterval of X minutes; performing a Fourier transformation on the curveof the heart beat rate average into a plurality of low-frequency degreesin a Fourier spectrum over a time interval of Y minutes; calculating arate of change of the low-frequency degrees in accordance with a timeinterval, wherein the rate of change is defined as the differencebetween the low-frequency degrees during a first time interval and thelow-frequency degrees during a second time interval; and determiningwhether the rate of change is smaller than a predetermined threshold Nof the rate of change to generate a drowsiness detecting result, whereinN is greater than 1, X ranges from 1 to 10, and Y ranges from 1 to 10.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the disclosureand, together with the description, serve to explain the principles ofthe invention.

FIG. 1 illustrates a flow chart of one embodiment of a predictivedrowsiness alarm method;

FIG. 2 illustrates a schematic view of one embodiment to raise alarmsupon occurrence of a condition;

FIG. 3 illustrates a flow chart of another embodiment of the predictivedrowsiness alarm method;

FIG. 4 illustrates a schematic view of another embodiment to raisealarms upon occurrence of another condition;

FIG. 5 illustrates a flow chart of another embodiment of the predictivedrowsiness alarm method;

FIG. 6 illustrates a schematic view of another embodiment to raisealarms upon occurrence of another condition;

FIG. 7 illustrates a flow chart of another embodiment of the predictivedrowsiness alarm method; and

FIG. 8 illustrates a flow chart of another embodiment of the predictivedrowsiness alarm method.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth.However, it should be understood that embodiments of the invention maybe practiced without these specific details. In other instances,well-known methods, structures and techniques have not been shown indetail in order not to obscure an understanding of this description.References to “the present embodiment,” “an embodiment,” “exemplaryembodiment,” “other embodiments,” etc. indicate that the embodiment(s)of the disclosure so described may include a particular feature,structure, or characteristic, but not every embodiment necessarilyincludes the particular feature, structure, or characteristic. Further,repeated use of the phrase “in the embodiment” does not necessarilyrefer to the same embodiment, although it may. Unless specificallystated otherwise, as apparent from the following discussions, it shouldbe appreciated that, throughout the specification, discussions utilizingterms such as “detecting,” “sensing,” “calculating,” “determining,”“judging,” “transforming,” “generating,” or the like refer to the actionand/or processes of a computer or computing system, or similarelectronic computing device, state machine and the like that manipulateand/or transform data represented as physical, such as electronic,quantities, into other data similarly represented as physicalquantities.

FIG. 1 discloses a flow chart of an exemplary embodiment of a predictivedrowsiness alarm method in non-contact mode; however, in anotherexemplary embodiment (not shown), the predictive drowsiness alarm methodcan be utilized in contact mode. In step S101, the detecting drowsinessmethod has been initiated. In step S102, the heart beat rate of a driveris sensed or detected (not shown). The heart beat rate refers to thefrequency of heartbeats or to the number of heartbeats per unit of time.The heart beat rate of the present embodiment can be measured by anelectrocardiographic device in contact mode or be detected by an ultrafrequency antenna in non-contact mode. In step S103, a curve of theheart beat rate average over a time interval of X minutes has beencalculated. In particular, the heart beat rate average over a timeinterval of X minutes means that the accumulated heart beat rates duringthe past X minutes are divided by X minutes to obtain the heart beatrate average at a particular moment. The individual points of theearlier heart beat rate averages are connected to illustrate a curve ofthe heart beat rate average over a time interval of X minutes, wherein Xranges from 1 to 10. In other words, the curve of the heart beat rateaverage can be illustrated in accordance with the earlier 1 to 10-minuteinterval, as shown in FIG. 2.

Referring to FIG. 1, in step S104, a plurality of slopes of the linearregression equations during a time interval of Y minutes can becalculated in accordance with the curve of the heart beat rate average.In particular, the heart beat rate average can be calculated accordingto the heart beat rates during the earlier Y-minute intervals. The slopeof the linear regression equations over a time interval of Y minutes canbe calculated in accordance with the heart beat rates during the past Yminutes. Consequently, each of the slopes of the linear regressionequations can be connected to illustrate a curve (not shown) of theslopes of the linear regression equations during a time interval of Yminutes, wherein Y ranges from 1 to 10. In other words, the curve of theslopes of the linear regression equations can be illustrated inaccordance with the earlier 1 to 10 minute intervals. In step S105, anaccumulated period of time has been determined. The slope values of thelinear regression equations in the accumulated period of time aresmaller than the predetermined slope value Z. In step S104, after eachof the slopes of the linear regression equations are calculated, theaccumulated period of time can be determined by adding up the slopeswhich are less than the predetermined slope value Z. For instance, ifthe slopes of the linear regression equations from 10:00:00 to 10:00:20are smaller than the predetermined slope Z, the accumulated period oftime is 20 seconds. However, if the slope of the linear regressionequation at 10:00:21 is greater than the predetermined slope Z, theaccumulated period of time will be interrupted. In this embodiment, thepredetermined slope Z ranges from −0.001 to −0.1. In addition, inanother embodiment (not shown), the time interval might range from 1second to 60 seconds.

As shown in FIG. 1, in step S106, the determination of whether thelength of the accumulated period of time is greater than a timethreshold T is performed so as to generate a drowsiness detectingresult. In particular, by performing step S105, the accumulated periodof time can be determined. If the accumulated period of time is 90seconds, for instance, the time threshold T of 60 seconds is smallerthan the length of the accumulated period of time so as to generate adrowsiness detecting result for raising an alarm. Therefore, the presentdisclosure can decide to perform the alarm raising step S107 or notbased on the drowsiness detecting result. After raising the alarm, thepredictive drowsiness detecting method including steps S101 to S107 willbe performed again. In another embodiment, if the accumulated period oftime is 90 seconds, for example, the time threshold T of 100 seconds islonger than the accumulated period of time so as to generate adrowsiness detecting result of not raising the alarm. Subsequently, thepredictive drowsiness detecting method will return to step S102 todetect the heart beat rate of the driver and to perform theabove-described steps again. Referring to the embodiment shown in FIG.1, the time threshold T ranges from 60 seconds to 600 seconds.

Referring to FIG. 2 showing the relation between heart beat rate andtime in the embodiment, the parameter X is 5, parameter Y is 5,parameter Z is −0.02, and parameter T is 300. As shown in FIG. 2, acurve C1 of the heart beat rate shows a decreasing trend, while a curveC2 of the heart beat rate average during a time interval of X minutesshows the same trend. After the steps of the method shown in FIG. 1 areperformed, there are several time points at which alarms are raised asshown in FIG. 2.

FIG. 3 shows another predictive drowsiness detecting method according toanother embodiment of the present disclosure. Steps S301, S302, S303 andS304 are similar to the above-mentioned steps S101, S102, S103 and S104,respectively. In step S305, a rate of change of the slopes of the linearregression equations is calculated based on a particular time interval.The rate of change is defined as the change between the slope of thelinear regression equation over the first time interval and the slope ofthe linear regression equation in the second time interval.Particularly, the slopes of the linear regression equations in each ofthe earlier intervals can be calculated and then the rate of change ofthe slope of the linear regression equations can be determined inaccordance with the slopes in each of the earlier time intervals. Forexample, the slope of the linear regression equation in the firstinterval (from 10:00:10 to 10:00:20) is −3 and the slope of the linearregression equation in the second interval (from 10:00:00 to 10:00:10)is −2. Thus, the rate of change is −1 (−3−(−2)=−1). In the embodiment,the time interval is 10 seconds; however, the time interval can bedesigned in accordance with other requirements. Preferably, the firsttime interval and the second time interval range from 1 second to 60seconds.

As shown in FIG. 3, in step S306, a determination whether the rate ofchange is smaller than a predetermined threshold M of the rate of changeis performed so as to generate a drowsiness detecting result.Particularly, the rate of change of the slopes of the linear regressionequation can be determined by step S305. For instance, if the rate ofchange is −1 and the predetermined threshold M of the rate of change is−0.1, the rate of change is obviously smaller than the predeterminedthreshold M. Thus, the drowsiness detecting result according to theabove-mentioned calculation will raise an alarm. Therefore, the methodof the present disclosure can decide whether to perform the alarmraising step S307 in accordance with the drowsiness detecting result.After raising the alarm, the predictive drowsiness detecting methodincluding steps S101 to S107 will be performed again. In anotherembodiment, if the rate of change is −0.01, for example, the threshold Mof the rate of change of −0.1 is obviously less than the rate of changeso as to generate a drowsiness detecting result of not raising an alarm.Subsequently, the predictive drowsiness detecting method will return tostep S302 to detect the heart beat rate of the driver and perform theabove-mentioned steps again. Referring to the embodiment shown in FIG.3, the threshold M of the rate of change ranges from −0.001 to −0.1.

Referring to FIG. 4 showing the relation between heart beat rate andtime in the embodiment, parameter X is 5, parameter Y is 5, andparameter M is −0.01. As shown in FIG. 4, a curve C3 of the heart beatrate shows a decreasing trend; meanwhile, a curve C4 of the heart beatrate average during a time interval of X minutes shows the same trend.After the steps of the method shown in FIG. 3 are performed, there areseveral time points at which alarms may be raised, as shown in FIG. 4.It is obvious that the time points at which alarms may be raised in themethod of FIG. 1 are different from those in the method of FIG. 3.

FIG. 5 shows another predictive drowsiness detecting method according toanother embodiment of the present disclosure. Steps S501, S502, and S503are similar to the above-mentioned steps S101, S102, and S103,respectively. In step S504, a Fourier transformation of the curve of theheart beat rate average is performed to obtain a plurality oflow-frequency degrees in a Fourier spectrum during a time interval of Yminutes. Particularly, the method of the present disclosure performs aFourier transformation (N=256) on the curve of the heart beat rateaverage into a plurality of low-frequency degrees in the Fourierspectrum. The unit of measure of the low-frequency is times per minute.The low-frequency ranges in the Fourier spectrum are selected from0.0005 Hz to 0.005 Hz, 0.0006 Hz to 0.004 Hz, 0.001 Hz to 0.0035 Hz,0.0018 Hz to 0.0025 Hz, and 0.003 Hz to 0.004 Hz. However, in anotherembodiment (not shown), the method of the present disclosure can utilizeother Fourier transformations to show the low-frequency degrees in otherFourier spectrums.

In step S505, a rate of change of the low-frequency degrees iscalculated in accordance with a particular time interval. The rate ofchange is defined as the difference between the low-frequency degreeduring the first time interval and the low-frequency degree during thesecond time interval. Particularly, the low-frequency degrees in each ofthe earlier intervals can be calculated and then the rate of change ofthe low-frequency degrees can be determined in accordance with thelow-frequency degrees in each of the earlier intervals. For example, thelow-frequency degree in the first time interval (from 10:00:10 to10:00:20) is 22 and the low-frequency degree in the second time interval(from 10:00:00 to 10:00:10) is 19. Thus, the rate of change is 3(22−19=3). In the embodiment, the time interval is 10 seconds; however,the time interval can be designed according to different requirements.Preferably, the first time interval and the second time interval rangefrom 1 second to 60 seconds.

As shown in FIG. 5, in step S506, the determination of whether the rateof change is greater than a predetermined threshold N of rate of changeis performed so as to generate a drowsiness detecting result. Inparticular, by performing step S505, the rate of change of thelow-frequency degrees can be determined. If the rate of change is 3 andthe predetermined threshold N of the rate of change is 2, for instance,the rate of change is obviously greater than the predetermined thresholdN so as to generate a drowsiness detecting result for raising an alarm.Therefore, the present disclosure can determine whether to perform thealarm raising step S507 in accordance with the drowsiness detectingresult. After raising the alarm, the predictive drowsiness detectingmethod including steps S501 to S507 will be performed again. In anotherembodiment, if the rate of change is 1 and the predetermined threshold Nof rate of change is 3, for example, the rate of change is obviouslyless than the predetermined threshold N so as to generate a drowsinessdetecting result of not raising an alarm. Subsequently, the predictivedrowsiness detecting method will return to step S502 to detect the heartbeat rate of the driver and to perform the above-mentioned steps again.Referring to the embodiment shown in FIG. 5, the predetermined thresholdN is greater than 1.

Referring to FIG. 6, which shows the relation between heart beat rateand time, parameter X is 5, parameter Y is 5, and parameter N is 4. Asshown in FIG. 6, a curve C5 of the heart beat rate does not shows atrend; meanwhile, a curve C6 of the heart beat rate average over a timeinterval of X minutes shows the same trend. After the steps of themethod shown in FIG. 5 are performed, there are several time points atwhich alarms may be raised, as shown in FIG. 6. It is obvious that thetime points at which alarms are raised are different among the method ofFIG. 1, the method of FIG. 3 and the method of FIG. 5.

The steps of the above-mentioned exemplary embodiment can be partiallycombined or totally combined to form another embodiment. As shown inFIG. 7, the entire set of steps can be combined. In the embodiment shownin FIG. 7, the steps prior to the judging steps S106, S306, and S506 arenot shown. In this embodiment, after the heart beat rate detecting stepis performed, a random number is generated to randomly determine whetherto perform the steps shown in FIG. 1, the steps shown in FIG. 3, or thesteps shown in FIG. 5; however, in another embodiment, the steps in FIG.1, the steps in FIG. 3, or the steps in FIG. 5 can be implemented inregular order. In the embodiment, when the requirement of steps S106,S306, or S506 is met, the alarms are raised to awake the driver. Afterthe alarms are raised or suppressed, the predictive drowsiness detectingmethod will detect the heart beat rate of the driver again.

In the embodiment shown in FIG. 8, the predictive drowsiness detectingmethod of the present disclosure can additionally include a weighingconcept. Steps S801 and S802 are similar to steps S101 and S102.Moreover, steps S803 and S804 serve to accumulate the detecting periodof X minutes and calculate the heart beat rate over a time interval of Xminutes and the heart beat rate average, similar to the functionsperformed in step S103. In step S804, a plurality of slopes of thelinear regression equations are calculated. Such slopes refer toindividual weighted values a in accordance with the accumulated periodof the different predetermined slopes Z. For instance, if theaccumulated period for which the slope of the linear regression equationis less than the predetermined slope (−0.02) exceeds 300 seconds, theweighted value a is defined as 0.3; if the accumulated period for whichthe slope of the linear regression equation is less than thepredetermined slope (−0.02) exceeds 350 seconds, the weighted value a isdefined as 0.6; if the accumulated period for which the slope of thelinear regression equation is less than the predetermined slope (−0.05)exceeds 300 seconds, the weighted value a is defined as 1. In thisembodiment, the system will retrieve the maximal weighted value a. Forexample, if the accumulated period for which the slope of the linearregression equation is less than the predetermined slope (−0.05) exceeds350 seconds, the weighted value a is 0.6 instead of 0.3. Furthermore,the weighted value b of the slope of the linear regression equation canbe calculated at the same time. For instance, if the rate of change ofthe slopes of the linear regression is less than −0.01 and theaccumulated period for which the slope of the linear regression equationis less than the predetermined slope (0) exceeds 30 seconds, theweighted value b is defined as 0.6. The weighted value a and theweighted value b can be summed up to determine weighted value A. Thus,step S805 is a determining step of the weighted value A. In step S806,the determination of whether the weighted value A is greater than orequal to the predetermined threshold (0.5) is performed. If the weightedvalue A is not greater than or equal to the predetermined threshold, theprocess returns to step S802. If the weighted value A is greater than orequal to the predetermined threshold, step S807 will be implemented. Instep S807, another weighted value B is determined. The rate of change oflow-frequency degrees is calculated by step S505 and refers to acorresponding weighted value B. After the weighted value B has beendetermined in accordance with the rate of change, step S808 isperformed. In step S808, it is determined whether the sum of theweighted value A and the weighted value B is greater than or equal to 1.If the sum of the weighted value A and the weighted value B is notgreater than or equal to 1, then the process will be returned to stepS802. If the sum of the weighted value A and the weighted value B is notgreater than or equal to 1, the alarms are raised to awake the driver.

The above-described embodiments of the present invention are intended tobe illustrative only. Numerous alternative embodiments may be devised bypersons skilled in the art without departing from the scope of thefollowing claims. Those skilled in the art may devise numerousalternative embodiments without departing from the scope of thefollowing claims.

1. A predictive drowsiness alarm method comprising the following steps:detecting a heart beat rate of a driver; calculating a curve of theheart beat rate average during a time interval of X minutes; calculatinga plurality of slopes of the linear regression equations during a timeinterval of Y minutes in accordance with the curve of the heart beatrate average; determining an accumulated period of time during which theslope values of the linear regression equations are smaller than thepredetermined slope value Z; and determining whether the length of theaccumulated period of time is greater than a time threshold T togenerate a drowsiness detecting result, wherein X ranges from 1 to 10, Yranges from 1 to 10, Z ranges from −0.001 to −0.1, and T ranges from 60to 600 seconds.
 2. The predictive drowsiness alarm method of claim 1,further comprising a step of determining whether to raise an alarm basedon the drowsiness detecting result.
 3. The predictive drowsiness alarmmethod of claim 1, further comprising a step of calculating, based on atime interval, the value of the accumulated period of time, wherein thetime interval ranges from 1 second to 60 seconds.
 4. The predictivedrowsiness alarm method of claim 3, further comprising a step ofcalculating, based on the time interval, a rate of change of the slopesof the linear regression equations, wherein the rate of change isdefined as the difference between the slope of the linear regressionequations during a first time interval and the slope of the linearregression equations during a second time interval.
 5. The predictivedrowsiness alarm method of claim 4, wherein the first time interval andthe second time interval range from 1 to 60 seconds.
 6. The predictivedrowsiness alarm method of claim 4, further comprising a step ofdetermining whether the rate of change is smaller than a predeterminedthreshold M of the rate of change to determine the drowsiness detectingresult, wherein the threshold M ranges from −0.001 to −0.1.
 7. Thepredictive drowsiness alarm method of claim 3, further comprising a stepof performing a Fourier transformation the curve of the heart beat rateaverage to obtain a plurality of low-frequency degrees in a Fourierspectrum over a time interval of Y minutes.
 8. The predictive drowsinessalarm method of claim 7, further comprising a step of calculating, basedon a time interval, a rate of change of the low-frequency degrees,wherein the rate of change is defined as the change in the low-frequencydegrees between a first time interval and a second time interval.
 9. Thepredictive drowsiness alarm method of claim 8, wherein the first timeinterval and the second time interval range from 1 to 60 seconds. 10.The predictive drowsiness alarm method of claim 8, further comprising astep of determining whether the rate of change is greater than apredetermined threshold N of the rate of change to determine thedrowsiness detecting result, wherein N is greater than
 1. 11. Thepredictive drowsiness alarm method of claim 8, wherein the low-frequencyranges from 0.0005 to 0.005 Hz.
 12. The predictive drowsiness alarmmethod of claim 1, wherein the step of calculating, based on the curveof the heart beat rate average, the slopes of the linear regressionequations over a time interval of Y minutes, wherein the slopes of thelinear regression equations are referred to an individual weighingvalue, and the predictive drowsiness alarm method further comprises astep of determining whether the weighing value is greater than apredetermined threshold to generate the drowsiness detecting result. 13.A predictive drowsiness alarm method, comprising the following steps:detecting a heart beat rate of a driver; calculating a curve of theheart beat rate average over a time interval of X minutes; calculating aplurality of slopes of the linear regression equations over a timeinterval of Y minutes in accordance with the curve of the heart beatrate average; calculating a rate of change of the slopes of the linearregression equations in accordance with a time interval, wherein therate of change is defined as the difference between the slope of thelinear regression equations during a first time interval and the slopeof the linear regression equations during a second time interval; anddetermining whether the rate of change is smaller than a predeterminedthreshold M of the rate of change to generate a drowsiness detectingresult, wherein M ranges from −0.001 to −0.1, X ranges from 1 to 10, andY ranges from 1 to
 10. 14. The predictive drowsiness alarm method ofclaim 13, further comprising a step of determining, according to thedrowsiness detecting result, of whether to raise an alarm.
 15. Thepredictive drowsiness alarm method of claim 13, wherein the first unitof time and the second unit of time range from 1 to 60 seconds.
 16. Thepredictive drowsiness alarm method of claim 13, further comprising astep of performing a Fourier transformation on the curve of the heartbeat rate average to obtain a plurality of low-frequency degrees in aFourier spectrum over a time interval of Y minutes.
 17. The predictivedrowsiness alarm method of claim 16, further comprising a step ofcalculating a rate of change, wherein the rate of change is defined asthe difference between the low-frequency degrees during the first timeinterval and the low-frequency degrees during the second time interval.18. The predictive drowsiness alarm method of claim 17, wherein thefirst time interval and the second time interval range from 1 to 60seconds.
 19. The predictive drowsiness alarm method of claim 17, furthercomprising a step of determining whether the rate of change is greaterthan a predetermined threshold N of the rate of change in order todetermine the drowsiness detecting result, wherein N is greater than 1.20. The predictive drowsiness alarm method of claim 17, wherein thelow-frequency ranges from 0.0005 to 0.005 Hz.
 21. The predictivedrowsiness alarm method of claim 13, wherein in the step of calculating,according to the curve of the heart beat rate average, the slopes of thelinear regression equations over a time interval of Y minutes, theslopes of the linear regression equations are referred to an individualweighted value, and the predictive drowsiness alarm method furthercomprises a step of determining whether the weighted value is greaterthan a predetermined threshold to generate the drowsiness detectingresult.
 22. A predictive drowsiness alarm method, comprising thefollowing steps: detecting a heart beat rate of a driver; calculating acurve of the heart beat rate average during a time interval of Xminutes; performing a Fourier transformation on the curve of the heartbeat rate average into a plurality of low-frequency degrees in a Fourierspectrum over a time interval of Y minutes; calculating a rate of changeof the low-frequency degrees in accordance with a time interval, whereinthe rate of change is defined as the difference between thelow-frequency degrees during a first time interval and the low-frequencydegrees during a second time interval; and determining whether the rateof change is smaller than a predetermined threshold N of the rate ofchange to generate a drowsiness detecting result, wherein N is greaterthan 1, X ranges from 1 to 10, and Y ranges from 1 to
 10. 23. Thepredictive drowsiness alarm method of claim 22, further comprising astep of determining, according to the drowsiness detecting result,whether to raise an alarm.
 24. The predictive drowsiness alarm method ofclaim 22, wherein the first time interval and the second time intervalrange from 1 to 60 seconds.
 25. The predictive drowsiness alarm methodof claim 22, wherein the low-frequency ranges from 0.0005 to 0.005 Hz.26. The predictive drowsiness alarm method of claim 22, wherein in thestep of calculating, according to the curve of the heart beat rateaverage, the slopes of the linear regression equations over a timeinterval of Y minutes, the slopes of the linear regression equations arereferred to an individual weighted value, and the predictive drowsinessalarm method further comprises a step of determining whether theweighted value is greater than a predetermined threshold to generate thedrowsiness detecting result.